In a global context marked by intensifying technological competition, geopolitical fragmentation and tighter control over strategic assets, industrial policy and corporate strategies are no longer confined to the economic domain. Organizations must learn to navigate the increasingly complex and unpredictable geo-economic landscape and as security logic and concerns come to the fore, knowledge, just as goods and people, does not flow freely anymore. Collaboration networks at all levels – and especially those devoted to research and innovation – are particularly affected, and organizations need to recalibrate their innovation and collaboration strategies. In the same ways as value chains reconfigure to fit this post-globalization landscape so it is for knowledge ecosystems that see their key firms, governments and universities engines pushed to reconfigure their partnerships. The pervasiveness of modern technologies and the increasing dependence of society functioning on such technologies extend the notion of dual use to almost all our everyday technologies. Moreover, even non-use of some of these technologies may become a risk. Artificial Intelligence is the most representative in this regard. From videogames to targets’ autonomous recognition to misinformation and hacking/bio-acceleration is a dual risk of both having and not having it.

This track aims to explore how models of Artificial Intelligence knowledge ecosystems and the government-industry-university interaction and research collaboration are being reshaped in an era of proactive industrial policies, foreign investment screening, controls on research cooperation, new regulatory frameworks on data governance and national security requirements. Specific attention will be devoted to technology transfer and AI-related trade, international academic partnerships, how firms, government and universities navigate emerging regimes of economic security and due diligence, and how these regimes affect the design and governance of AI projects, the concept and reconfiguration of AI-related value chains and knowledge ecosystems.

Overall, the track pursues two main objectives: first, to assess how the increasing geo-economic confrontation may affect the competitiveness, sustainability and resilience of AI ecosystems and second, to envisage and analyse policy instruments, governance architectures and organizational structures that can reconcile critical tensions in the key government, industry and University engines of AI knowledge ecosystems.